Data mining multimedia softcomputing

First title to ever present soft computing approaches and their application in data mining, along with the traditional hard-computing approaches* Addresses the principles of multimedia data compression techniques (for image, video, text) and their role in data mining* Discusses principles and classical algorithms on string matching and their role in data mining

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Data Mining: Multimedia, Soft Computing, and Bioinformatics
By Sushmita Mitra, Tinku Acharya
Contributor Sushmita Mitra, Tinku Acharya

Data Mining Techniques

Learn how to quickly and easily access the wealth of information in your information systems With data mining, companies can analyze customers' past behaviors in order to make strategic decisions for the future. This book is a practical guide to mining business data to help marketers and business managers focus their marketing and sales strategies. It explains how each mining technique works and what kinds of business problems each one can solve.

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Data Mining Techniques: For Marketing, Sales, and Customer Support
By Michael J. A. Berry, Gordon Linoff
Contributor Gordon Linoff

Data Mining for Business Intelligence

Learn how to develop models for classification, prediction, and customer segmentation with the help of Data Mining for Business IntelligenceIn today's world, businesses are becoming more capable of accessing their ideal consumers, and an understanding of data mining contributes to this success. Data Mining for Business Intelligence, which was developed from a course taught at the Massachusetts Institute of Technology's Sloan School of Management, and the University of Maryland's Smith School of Business, uses real data and actual cases to illustrate the applicability of data mining intelligence to the development of successful business models.Featuring XLMiner, the Microsoft Office Excel add-in, this book allows readers to follow along and implement algorithms at their own speed, with a minimal learning curve. In addition, students and practitioners of data mining techniques are presented with hands-on, business-oriented applications. An abundant amount of exercises and examples are provided to motivate learning and understanding.Data Mining for Business Intelligence:* Provides both a theoretical and practical understanding of the key methods of classification, prediction, reduction, exploration, and affinity analysis* Features a business decision-making context for these key methods* Illustrates the application and interpretation of these methods using real business cases and dataThis book helps readers understand the beneficial relationship that can be established between data mining and smart business practices, and is an excellent learning tool for creating valuable strategies and making wiser business decisions.

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Data mining for business intelligence: concepts, techniques, and applications in Microsoft Office Excel with XLMiner
By Galit Shmueli, Nitin R. Patel, Peter C. Bruce
Contributor Galit Shmueli, Nitin R. Patel, Peter C. Bruce

Data Mining

This book offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data miningincluding both tried-and-true techniques of the past and Java-based methods at the leading edge of contemporary research. If you're involved at any level in the work of extracting usable knowledge from large collections of data, this clearly written and effectively illustrated book will prove an invaluable resource. Complementing the authors' instruction is a fully functional platform-independent Java software system for machine learning, available for download. Apply it to the sample data sets provided to refine your data mining skills, apply it to your own data to discern meaningful patterns and generate valuable insights, adapt it for your specialized data mining applications, or use it to develop your own machine learning schemes. * Helps you select appropriate approaches to particular problems and to compare and evaluate the results of different techniques. * Covers performance improvement techniques, including input preprocessing and combining output from different methods. * Comes with downloadable machine learning software: use it to master the techniques covered inside, apply it to your own projects, and/or customize it to meet special needs.

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Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations
By Ian H. Witten, Eibe Frank
Contributor Eibe Frank

High Performance Data Mining

High Performance Data Mining: Scaling Algorithms, Applications and Systems brings together in one place important contributions and up-to-date research results in this fast moving area. High Performance Data Mining: Scaling Algorithms, Applications and Systems serves as an excellent reference, providing insight into some of the most challenging research issues in the field.

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High Performance Data Mining: Scaling Algorithms, Applications, and Systems
By Yike Guo, Robert Grossman
Contributor Yike Guo, Robert Grossman

Introduction to Data Mining

Presents fundamental concepts and algorithms for those learning data mining for the first time. This book explores each concept and features each major topic organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.

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Introduction to Data Mining
By Pang-Ning Tan, Michael Steinbach, Vipin Kumar

Data Mining

As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more. + Algorithmic methods at the heart of successful data mining -- including tried and true techniques as well as leading edge methods; + Performance improvement techniques that work by transforming the input or output; + Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization -- in a new, interactive interface.

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Data Mining: Practical Machine Learning Tools and Techniques
By Ian H. Witten, Eibe Frank
Contributor Eibe Frank